Conversational Biometrics technology enables a non-intrusive and highly accurate mechanism for determining and authenticating speaker identities, based on the analysis of their voice. Conversational Biometrics combines acoustic text-independent speaker recognition with additional verification sources such as spoken knowledge to create the most flexible and robust speaker verification and detection.
Unlike other biometrics, voice contains multiple sources of information that can be acquired using existing ubiquitous infrastructure and used for recognizing and verifying speaker identities. The primary source is the speaker's voiceprint, which can be analyzed purely from an acoustic perspective, without considering the content being spoken. In addition to the voiceprint, voice also contains information on speaker's knowledge, and with an integrated conversational interface, the same voice can be analyzed twice: once for voiceprint match, and again for knowledge match.
Contemporary speaker recognition systems, such as those described in G. N. Ramaswamy, R. D. Zilca, O. Alecksandrovich, “A Programmable Policy Manager For Conversational Biometrics”, EUROSPEECH-2003, Geneve, Switzerland, September, 2003, hereinafter referred to as (“Ramaswamy”) and L. P. Heck, D. Genoud, “Combining Speaker and Speech Recognition Systems”, ICSLP 2002, Denver, September, 2002, depend on a multiplicity of information sources which provide evidence for the assessment of a speaker's identity. Conversational Biometrics is one such system (see Ramaswamy); it relies on a speaker's acoustic characteristics as well as the speaker's anticipated level of knowledge. Chief among the benefits of this approach are:                The ability to compensate for corruption of any one source; and        Increased confidence in the result due to independent corroborative information, as described in Ramaswamy and U. V. Chaudhari, J. Navratil, G. N. Ramaswamy, R. D. Zilca “Future Speaker Recognition Systems: Challenges and Solutions”, Proc. of AUTOID-2002, Tarrytown, N.Y., March 2002.It is also possible that the various sources could provide contradictory evidence, which on the surface would make the results inconclusive. However, context may be able to disambiguate the results.        
Thus, to effectively use all of the information available, there must exist a method for reconciling such contradictory evidence in a policy that guides the analysis of a Conversational Biometrics verification system.